Revision as of 07:47, 6 July 2025

Data Representation

Introduction

Data representation is a fundamental concept in the fields of computer science and information systems. It refers to the methods and techniques employed to encode, manipulate, and interpret data within a computing environment. The primary objective of data representation is to enable efficient data storage, retrieval, and processing, which are crucial for the development of software applications, databases, and the broader field of technology. Understanding data representation is essential for both the design of computer systems and the practical application of data sciences.

Data can take various forms, including binary, textual, numerical, graphical, and more. It is critical to choose the appropriate representation for any given task, as it affects performance, usability, and the overall integrity of information.

History

Early Developments

The origins of data representation can be traced back to early computing languages and binary systems, where data was primarily represented in a binary format—using combinations of 0s and 1s. This representation aligns with the foundational principles of digital computing, as electronic circuits can easily distinguish between two states: on (1) and off (0).

In the mid-20th century, foundational programming languages emerged, such as Fortran and COBOL, which introduced more complex data structures like arrays and records. These languages allowed data representation of more than just numerical values, paving the way for structured programming and data management.

Advancements in Data Formats

As computer technology advanced, specialized data formats were developed to enhance the representation of information. For example, the development of markup languages such as HTML in the late 20th century provided a way to represent structured documents on the World Wide Web, promoting interactivity and multimedia content.

The introduction of databases also revolutionized data representation, leading to the creation of various data models like relational, hierarchical, and object-oriented models. These models enabled more efficient organization and manipulation of data within systems, accommodating a diversity of data types and queries.

Types of Data Representation

Binary Representation

Binary is the most fundamental form of data representation in computing. In this system, all data is represented using only two digits: 0 and 1. Each binary digit (or bit) is a basic unit of information. Multiple bits can be combined to represent larger data types. For example, a byte consists of 8 bits, allowing for 256 unique values (2^8).

In binary, integers, floating-point numbers, characters, and various data structures are represented using specific encoding schemes. Common encoding systems include:

ASCII (American Standard Code for Information Interchange): Encodes 128 characters using 7 bits.
UTF-8: A variable-width character encoding that can encode every character in the Unicode character set.
IEEE 754: A standard for representing floating-point numbers in binary.

Text Representation

Textual data representation involves encoding character strings and symbols, which are often used in conjunction with markup languages. Text representation is vital for producing meaningful output in applications such as document creation, web pages, and user interfaces.

In addition to ASCII and UTF-8, other encoding systems like UTF-16 and ISO/IEC 8859 are used, particularly in regions with different alphabets and characters. Complex textual representations must also consider language nuances, such as diacritics or language-specific glyphs.

Numerical Representation

Numerical data can be represented in different ways based on the range and precision required, particularly:

**Integer Representation**: Can be represented in various formats, including signed and unsigned integers. The two's complement method is often used for signed integers, which involves inverting the bits and adding one to represent negative values.
**Floating-Point Representation**: Enables the representation of real numbers, including fractions. The IEEE 754 standard distinguishes between single-precision (32 bits) and double-precision (64 bits) representations, accommodating a wide range of values.

Usage and Implementation

Data Structures

Data representation is inherently tied to data structures—the methods used to organize and store data effectively. Common data structures include:

**Arrays**: Fixed-size collections of elements, often of the same type, providing indexed access.
**Linked Lists**: Composed of nodes where each node points to the next, enabling dynamic memory allocation.
**Trees**: Hierarchical structures with nodes connected in parent-child relationships, widely used in application domains like databases and XML parsing.
**Graphs**: Consist of vertices and edges, representing a set of connections, useful in networking and pathfinding applications.

Each of these structures influences how data is represented, stored in memory, and accessed, directly impacting performance and computational efficiency.

File Formats

Data representation extends to file formats that dictate how information is structured and stored in files. Various formats serve different purposes, including:

**Text Files**: Store unformatted text, readable by humans. Formats include .txt and .csv.
**Binary Files**: Use binary encoding, suitable for images, audio, and other multimedia content; formats such as .jpg, .png, and .mp3.
**Markup Files**: Structured representations such as XML and JSON, commonly used for data interchange between systems.

Understanding the implications of different file formats is crucial when considering data integrity, conversion, and compatibility across platforms and applications.

Data Serialization

Data serialization is the process of converting data structures or object state into a format that can be easily stored or transmitted and then reconstructed later. This is particularly relevant in network communication and persistent storage.

Common serialization formats include:

**JSON (JavaScript Object Notation)**: A lightweight and human-readable representation of structured data, widely used for web APIs.
**XML (Extensible Markup Language)**: A flexible format that uses tags to encode documents, allowing for complex data structures and hierarchical relationships.
**Protocol Buffers**: Developed by Google, it is a method for serializing structured data, offering efficient storage and transmission, particularly for large-scale applications.

Real-world Examples

Data Representation in Programming

Data representation is crucial in programming languages, influencing how developers interact with data. For example, Python's data types (e.g., lists, tuples, dictionaries) provide developers with high-level abstractions to work with data, while C allows for fine-tuning through direct memory manipulation with structs and unions. This diversity reflects different paradigms, such as object-oriented, functional, and procedural programming, underscoring how data representation shapes coding practices.

Database Management Systems (DBMS)

In databases, data representation is critical for optimizing data access and manipulation through structured query language (SQL) in relational databases like MySQL and PostgreSQL. Different types of databases, such as NoSQL databases like MongoDB and Cassandra, choose alternative representation strategies based on specific application requirements, focusing on document-oriented and wide-column stores.

For instance, NoSQL databases often represent data as key-value pairs or as documents, which enables scalability and flexibility for large datasets and unstructured data.

User Interfaces and Data Visualization

Data representation is vital in user interfaces and data visualization, where the visual encoding of information governs how effectively users comprehend and interact with data. Graphs, charts, and tables serve as primary tools for representing statistical information.

For example, bar charts represent categorical data, while line graphs showcase trends over time. Tools like Tableau and Power BI are employed widely in businesses to convert raw data into actionable insights through visual representation.

Criticism and Controversies

Privacy and Security Concerns

The representation of data often raises critical issues regarding privacy and security, particularly with the increasing amounts of personal information being collected and stored. Poor data representation can lead to vulnerabilities, making systems exposed to data breaches, unauthorized access, and misuse of sensitive information.

For instance, inadequate data anonymization practices can allow malicious actors to re-identify individuals from supposedly anonymized datasets, undermining privacy efforts. Legal frameworks, such as the General Data Protection Regulation (GDPR) in Europe, emphasize the importance of secure data representation and management to safeguard individuals' privacy rights.

Misrepresentation and Bias

Data can be misrepresented or manipulated, leading to biased interpretations that can influence decision-making in businesses, politics, and society. For example, selective data representation, such as cherry-picking data points to support a specific argument, can distort reality.

Critics have underscored the ethical implications of data visualization, especially when dealing with complex datasets. Well-designed visualizations should strive for impartiality and accuracy to ensure they do not mislead stakeholders or the public.

Obsolescence and Compatibility Issues

As technology evolves, older data representation methods may become obsolete or incompatible with newer systems, leading to challenges in data migration and integration. For instance, legacy systems that utilize outdated binary formats or proprietary data structures can impede modernization efforts, ultimately resulting in increased costs and reduced efficiency.

The shift from older file formats like .doc to more versatile formats like .docx or .odt illustrates the necessity of adapting data representation strategies to keep pace with technological advancements.

Influence and Impact

Data representation has profound implications across various domains, from technology and business to scientific research and social initiatives. Its influence can be observed in several key areas:

Advancements in Artificial Intelligence

In artificial intelligence (AI) and machine learning, effective data representation is pivotal for training models and improving outcomes. High-dimensional data representations, like embeddings used in natural language processing, enable systems to comprehend context and meaning, facilitating tasks such as sentiment analysis and chatbots.

The quality of the chosen data representation directly affects model performance, influencing the results of AI applications across industries, from healthcare diagnostics to autonomous vehicles.

Innovation in Data Analytics

Data analytics relies heavily on effective data representation to uncover insights, trends, and patterns. Organizations utilize advanced data representation techniques, such as dimensionality reduction methods (like PCA) and clustering algorithms, to visualize and interpret large datasets efficiently.

The increasing availability of big data emphasizes the importance of innovative representation strategies to extract valuable knowledge while minimizing noise and redundancy.

Role in Education and Research

In academia, data representation is integral to research methodologies, where the accuracy and effectiveness of data presentation can enhance scientific communication. Research papers, databases, and educational materials heavily rely on standardized data representations to share findings, promote reproducibility, and foster collaboration among different disciplines.

The continuous evolution of data representation techniques contributes to the advancement of knowledge across fields, from computational biology to social sciences.

References

@@ Line 2: / Line 2: @@
 == Introduction ==
-Data representation is a foundational concept in the fields of computer science, mathematics, and information technology, referring to the methods used to encode and store different types of information in a format that can be easily processed, analyzed, and communicated by computer systems. This concept encompasses a variety of data types, structures, and visualization techniques aimed at effectively conveying information while maintaining accuracy and integrity.
+Data representation is a fundamental concept in the fields of computer science and information systems. It refers to the methods and techniques employed to encode, manipulate, and interpret data within a computing environment. The primary objective of data representation is to enable efficient data storage, retrieval, and processing, which are crucial for the development of software applications, databases, and the broader field of technology. Understanding data representation is essential for both the design of computer systems and the practical application of data sciences.
-In essence, data representation serves as the interface between the raw data generated in various contexts and the functional requirements of applications that utilize that data. It plays a critical role in defining how data is structured, stored, and manipulated, thus impacting performance, interoperability, and efficiency across systems.
+Data can take various forms, including binary, textual, numerical, graphical, and more. It is critical to choose the appropriate representation for any given task, as it affects performance, usability, and the overall integrity of information.
-== History or Background ==
+== History ==
-The concept of data representation has evolved significantly since the early days of computing. Initially, data was represented in simplistic forms, such as binary codes utilized by the first electronic computers in the mid-20th century. The choice of binary representation, based on two states (0 and 1), was largely influenced by the design of electronic circuits which could easily represent two levels of voltage.
-As technology advanced, the need for more complex data types emerged. In the 1960s and 1970s, programming languages such as FORTRAN and COBOL introduced structured data types, enabling developers to represent records and files more effectively. The introduction of relational databases in the 1980s and the SQL language further transformed data representation, allowing for more sophisticated data structures like tables, which facilitated complex queries and data relationships.
+=== Early Developments ===
+The origins of data representation can be traced back to early computing languages and binary systems, where data was primarily represented in a binary format—using combinations of 0s and 1s. This representation aligns with the foundational principles of digital computing, as electronic circuits can easily distinguish between two states: on (1) and off (0).
-The rise of the internet and web technologies in the 1990s brought about new data representations. Hypertext Markup Language (HTML) and Extensible Markup Language (XML) allowed for greater flexibility in how information was represented and shared across different platforms. The introduction of JSON (JavaScript Object Notation) in the 2000s revolutionized data interchange on the web, allowing for simpler and more human-readable data format.
+In the mid-20th century, foundational programming languages emerged, such as Fortran and COBOL, which introduced more complex data structures like arrays and records. These languages allowed data representation of more than just numerical values, paving the way for structured programming and data management.
-== Design or Architecture ==
+=== Advancements in Data Formats ===
-Data representation involves various design principles and architectures that determine how data is organized and accessed. These can be broken down into several key aspects:
+As computer technology advanced, specialized data formats were developed to enhance the representation of information. For example, the development of markup languages such as HTML in the late 20th century provided a way to represent structured documents on the World Wide Web, promoting interactivity and multimedia content.
-=== Data Types ===
+The introduction of databases also revolutionized data representation, leading to the creation of various data models like relational, hierarchical, and object-oriented models. These models enabled more efficient organization and manipulation of data within systems, accommodating a diversity of data types and queries.
-Data types are the fundamental building blocks of data representation. Common data types include:
-* '''Primitive Types''': Basic data units such as integers, floating-point numbers, characters, and boolean values.
+== Types of Data Representation ==
-* '''Complex Types''': Data structures that can encapsulate multiple primitive types, such as arrays, lists, sets, and dictionaries.
+=== Binary Representation ===
+Binary is the most fundamental form of data representation in computing. In this system, all data is represented using only two digits: 0 and 1. Each binary digit (or bit) is a basic unit of information. Multiple bits can be combined to represent larger data types. For example, a byte consists of 8 bits, allowing for 256 unique values (2^8).
+In binary, integers, floating-point numbers, characters, and various data structures are represented using specific encoding schemes. Common encoding systems include:
+* ASCII (American Standard Code for Information Interchange): Encodes 128 characters using 7 bits.
+* UTF-8: A variable-width character encoding that can encode every character in the Unicode character set.
+* IEEE 754: A standard for representing floating-point numbers in binary.
+=== Text Representation ===
+Textual data representation involves encoding character strings and symbols, which are often used in conjunction with markup languages. Text representation is vital for producing meaningful output in applications such as document creation, web pages, and user interfaces.
+In addition to ASCII and UTF-8, other encoding systems like UTF-16 and ISO/IEC 8859 are used, particularly in regions with different alphabets and characters. Complex textual representations must also consider language nuances, such as diacritics or language-specific glyphs.
+=== Numerical Representation ===
+Numerical data can be represented in different ways based on the range and precision required, particularly:
+* **Integer Representation**: Can be represented in various formats, including signed and unsigned integers. The two's complement method is often used for signed integers, which involves inverting the bits and adding one to represent negative values.
+* **Floating-Point Representation**: Enables the representation of real numbers, including fractions. The IEEE 754 standard distinguishes between single-precision (32 bits) and double-precision (64 bits) representations, accommodating a wide range of values.
+== Usage and Implementation ==
 === Data Structures ===
-Data structures are more complex arrangements of data that facilitate efficient storage and retrieval. They include:
+Data representation is inherently tied to data structures—the methods used to organize and store data effectively. Common data structures include:
-* '''Arrays''': A collection of items stored at contiguous memory locations.
+* **Arrays**: Fixed-size collections of elements, often of the same type, providing indexed access.
-* '''Linked Lists''': A collection of nodes that represent a sequence, where each node points to the next.
+* **Linked Lists**: Composed of nodes where each node points to the next, enabling dynamic memory allocation.
-* '''Trees and Graphs''': Hierarchical and network-based structures that represent relationships between data elements.
+* **Trees**: Hierarchical structures with nodes connected in parent-child relationships, widely used in application domains like databases and XML parsing.
+* **Graphs**: Consist of vertices and edges, representing a set of connections, useful in networking and pathfinding applications.
+Each of these structures influences how data is represented, stored in memory, and accessed, directly impacting performance and computational efficiency.
+=== File Formats ===
+Data representation extends to file formats that dictate how information is structured and stored in files. Various formats serve different purposes, including:
+* **Text Files**: Store unformatted text, readable by humans. Formats include .txt and .csv.
+* **Binary Files**: Use binary encoding, suitable for images, audio, and other multimedia content; formats such as .jpg, .png, and .mp3.
+* **Markup Files**: Structured representations such as XML and JSON, commonly used for data interchange between systems.
+Understanding the implications of different file formats is crucial when considering data integrity, conversion, and compatibility across platforms and applications.
-=== Encoding Schemes ===
+=== Data Serialization ===
-Encoding schemes define how data types are translated into binary format. Examples include:
+Data serialization is the process of converting data structures or object state into a format that can be easily stored or transmitted and then reconstructed later. This is particularly relevant in network communication and persistent storage.
-* '''ASCII (American Standard Code for Information Interchange)''': A character encoding standard that uses 7 bits to represent characters.
-* '''UTF-8 (8-bit Unicode Transformation Format)''': A variable-length character encoding system for Unicode characters, which can represent every character in the Unicode character set.
-=== Serialization ===
+Common serialization formats include:
-Serialization is the process of converting complex data structures into a format suitable for storage or transmission. Common serialization formats include:
+* **JSON (JavaScript Object Notation)**: A lightweight and human-readable representation of structured data, widely used for web APIs.
-* '''XML''': A markup language that encodes data in a structured format.
+* **XML (Extensible Markup Language)**: A flexible format that uses tags to encode documents, allowing for complex data structures and hierarchical relationships.
-* '''JSON''': A lightweight format for data interchange that enables easy readability and programmability.
+* **Protocol Buffers**: Developed by Google, it is a method for serializing structured data, offering efficient storage and transmission, particularly for large-scale applications.
-* '''Protocol Buffers''': A method developed by Google for serializing structured data in a more efficient binary format.
-== Usage and Implementation ==
+== Real-world Examples ==
-Data representation is integral to various sectors that require data management, processing, and presentation.
+=== Data Representation in Programming ===
+Data representation is crucial in programming languages, influencing how developers interact with data. For example, Python's data types (e.g., lists, tuples, dictionaries) provide developers with high-level abstractions to work with data, while C allows for fine-tuning through direct memory manipulation with structs and unions. This diversity reflects different paradigms, such as object-oriented, functional, and procedural programming, underscoring how data representation shapes coding practices.
-=== In Computing ===
+=== Database Management Systems (DBMS) ===
-Within computing, data representation is crucial for programming languages, where the syntax and semantics define how data is constructed, manipulated, and accessed. Different programming paradigms (such as object-oriented, functional, and procedural programming) utilize distinct data representation approaches to achieve efficiency and clarity in code structure.
+In databases, data representation is critical for optimizing data access and manipulation through structured query language (SQL) in relational databases like MySQL and PostgreSQL. Different types of databases, such as NoSQL databases like MongoDB and Cassandra, choose alternative representation strategies based on specific application requirements, focusing on document-oriented and wide-column stores.
-=== In Databases ===
+For instance, NoSQL databases often represent data as key-value pairs or as documents, which enables scalability and flexibility for large datasets and unstructured data.
-Data representation plays a pivotal role in database management systems (DBMS). The choice of data model (relational, NoSQL, graph, etc.) fundamentally affects how data is represented and how queries are formulated. Furthermore, normalization techniques ensure data integrity and reduce redundancy through well-structured representation.
-=== In Data Visualization ===
+=== User Interfaces and Data Visualization ===
-Data representation extends to the visual domain, where graphical representations of data, such as charts, graphs, and dashboards, aim to effectively communicate insights and analyses. Data visualization tools utilize different encoding techniques (color, size, shape) to represent and emphasize data patterns.
+Data representation is vital in user interfaces and data visualization, where the visual encoding of information governs how effectively users comprehend and interact with data. Graphs, charts, and tables serve as primary tools for representing statistical information.
-=== In Communication Protocols ===
+For example, bar charts represent categorical data, while line graphs showcase trends over time. Tools like Tableau and Power BI are employed widely in businesses to convert raw data into actionable insights through visual representation.
-Network communications and protocols rely on standard data representations for transmitting data across devices. Formats such as HTTP, TCP/IP, and others specify how data packets should be formatted to ensure coherent interaction and reliable delivery between nodes within a network.
-== Real-world Examples or Comparisons ==
+== Criticism and Controversies ==
-Real-world applications of data representation can be observed across various domains, illustrating how different formats and structures are employed to meet specific requirements:
-=== Social Media ===
+=== Privacy and Security Concerns ===
-Social media platforms utilize JSON for APIs to facilitate data interchange between client and server, allowing for seamless content retrieval, publishing, and interaction. User-generated content is represented as structured data in platforms like Twitter and Facebook, making it easier to analyze trends and behaviors.
+The representation of data often raises critical issues regarding privacy and security, particularly with the increasing amounts of personal information being collected and stored. Poor data representation can lead to vulnerabilities, making systems exposed to data breaches, unauthorized access, and misuse of sensitive information.
-=== Financial Systems ===
+For instance, inadequate data anonymization practices can allow malicious actors to re-identify individuals from supposedly anonymized datasets, undermining privacy efforts. Legal frameworks, such as the General Data Protection Regulation (GDPR) in Europe, emphasize the importance of secure data representation and management to safeguard individuals' privacy rights.
-In financial services, data representation is critical for modeling transactions, accounts, and customer information. For instance, relational databases are widely used to represent transactional data, allowing institutions to process complex queries for account balances, transaction histories, and fraud detection.
-=== E-commerce ===
+=== Misrepresentation and Bias ===
-E-commerce platforms leverage structured data representation schemas like schema.org for product listings, enabling search engines to better understand and index product information. This representation improves visibility and enhances the user experience through clearer information delivery.
+Data can be misrepresented or manipulated, leading to biased interpretations that can influence decision-making in businesses, politics, and society. For example, selective data representation, such as cherry-picking data points to support a specific argument, can distort reality.
-=== Geographic Information Systems (GIS) ===
+Critics have underscored the ethical implications of data visualization, especially when dealing with complex datasets. Well-designed visualizations should strive for impartiality and accuracy to ensure they do not mislead stakeholders or the public.
-In GIS, data representation is essential for encoding spatial data. Vector and raster data representations are commonly used to represent geographical features and characteristics, allowing for advanced mapping and geographic analysis.
-== Criticism or Controversies ==
+=== Obsolescence and Compatibility Issues ===
-Despite its importance, data representation has faced criticism and challenges that can impact its efficacy and reliability:
+As technology evolves, older data representation methods may become obsolete or incompatible with newer systems, leading to challenges in data migration and integration. For instance, legacy systems that utilize outdated binary formats or proprietary data structures can impede modernization efforts, ultimately resulting in increased costs and reduced efficiency.
-=== Ambiguity ===
+The shift from older file formats like .doc to more versatile formats like .docx or .odt illustrates the necessity of adapting data representation strategies to keep pace with technological advancements.
-One of the key challenges in data representation is ensuring that data remains unambiguous and accurately reflects the underlying information. Poorly designed encoding schemes or representations can lead to misinterpretation and errors, particularly in applications requiring precision, such as healthcare and aviation.
-=== Data Overhead ===
+== Influence and Impact ==
-Certain data representation formats can introduce significant overhead in terms of storage and transmission. For example, verbose formats like XML can consume more bandwidth compared to more compact representations like binary or JSON, potentially affecting performance in resource-constrained environments.
-=== Accessibility and Inclusivity ===
+Data representation has profound implications across various domains, from technology and business to scientific research and social initiatives. Its influence can be observed in several key areas:
-Data representation also raises concerns about accessibility. Certain formats may not be universally accessible, particularly for individuals with disabilities. The design of data representation systems must consider diverse user needs to promote inclusivity and prevent information barriers.
-=== Security and Privacy ===
+=== Advancements in Artificial Intelligence ===
-With the rise of big data and analytics, data representation related to sensitive information has sparked debates on security and privacy. Inadequate representation of data can expose vulnerabilities, potentially leading to data breaches or misuse. Consequently, proper data handling and representation protocols are essential for protecting user privacy.
+In artificial intelligence (AI) and machine learning, effective data representation is pivotal for training models and improving outcomes. High-dimensional data representations, like embeddings used in natural language processing, enable systems to comprehend context and meaning, facilitating tasks such as sentiment analysis and chatbots.
-== Influence or Impact ==
+The quality of the chosen data representation directly affects model performance, influencing the results of AI applications across industries, from healthcare diagnostics to autonomous vehicles.
-Data representation has had a profound impact on various sectors:
-=== Innovation in Technology ===
+=== Innovation in Data Analytics ===
-The evolution of data representation methods has driven innovations in computer science and technology, encouraging the development of programming languages, database systems, and web technologies that prioritize efficient data handling.
+Data analytics relies heavily on effective data representation to uncover insights, trends, and patterns. Organizations utilize advanced data representation techniques, such as dimensionality reduction methods (like PCA) and clustering algorithms, to visualize and interpret large datasets efficiently.
-=== Big Data and Machine Learning ===
+The increasing availability of big data emphasizes the importance of innovative representation strategies to extract valuable knowledge while minimizing noise and redundancy.
-As data generation continues to accelerate, effective data representation is critical in machine learning and artificial intelligence. Techniques such as feature encoding and dimensionality reduction influence the performance of predictive models, highlighting the importance of suitable representation in these advanced fields.
-=== Societal Changes ===
+=== Role in Education and Research ===
-In a data-driven society, how data is represented influences decision-making across numerous domains, including healthcare, education, and governance. Proper representation facilitates informed choices and contributes to transparency, accountability, and improved service delivery.
+In academia, data representation is integral to research methodologies, where the accuracy and effectiveness of data presentation can enhance scientific communication. Research papers, databases, and educational materials heavily rely on standardized data representations to share findings, promote reproducibility, and foster collaboration among different disciplines.
-=== Scientific Research ===
+The continuous evolution of data representation techniques contributes to the advancement of knowledge across fields, from computational biology to social sciences.
-In research fields, data representation is vital for experimental data organization and analysis. Accurate representation of data through charts, graphs, and tables enhances communication of findings and supports reproducibility in scientific inquiry.
-== See also ==
+== See Also ==
-* [[Data structure]]
+* [[Data Structure]]
-* [[Data model]]
+* [[File Format]]
-* [[Binary representation]]
+* [[Serialization]]
-* [[Data serialization]]
+* [[Data Visualization]]
-* [[Information architecture]]
+* [[Big Data]]
-* [[Data visualization]]
+* [[Artificial Intelligence]]
-* [[Big data]]
+* [[Database Management Systems]]
 == References ==
-* [https://www.itu.int/en/ITU-T/focusgroups/ai/Pages/default.aspx ITU Focus Group on Artificial Intelligence for Data Representation]
+* [https://www.w3.org/standards/ - World Wide Web Consortium (W3C)]
-* [https://www.w3.org/XML/ XML Official Specification]
+* [https://www.ietf.org/rfc/rfc3629.txt - UTF-8 Encoding Specification]
-* [https://json.org/ JSON - The Data Format of JavaScript]
+* [https://en.wikipedia.org/wiki/IEEE_754 - IEEE 754 Standard for Floating-Point Arithmetic]
-* [https://www.iso.org/iso-4217-currency-codes.html ISO 4217 Currency Codes]
+* [https://www.dataversity.net/ - Dataversity - Data Strategy and Education]
-* [https://www.ibm.com/cloud/learn/data-visualization IBM Cloud: Data Visualization Security]
+* [https://gdpr.eu/ - General Data Protection Regulation (GDPR) Resources]
-* [https://www.acm.org/publications/turing-awards Turing Awards: The Influence of Data Representation in Computer Science]
+* [https://www.tableau.com/ - Tableau Software for Data Visualization]
+* [https://powerbi.microsoft.com/ - Microsoft Power BI]
+* [https://www.tensorflow.org/ - TensorFlow - Machine Learning Framework]
+* [https://www.kdnuggets.com/ - KDnuggets - Data Science and Machine Learning Resources]
-[[Category:Data representation]]
+[[Category:Data structures]]
 [[Category:Computer science]]
-[[Category:Information science]]
+[[Category:Information theory]]